Algorithm REF  describes transformation sampling. By keeping only one sample (\(m = 1\) ) we recover the usual augmentation setting, that yields one augmentation per sample in the training batch. In our experiments we address this setting, since it has lower computational costs. However, simply keeping more samples from the chain (\(m>1\) ) allows to extend the method to the batch augmentation setting [1]}, which creates several augmented samples from the same instance in each batch.
